Efficient and Flexible Cluster-and-Search for CBIR

Content-Based Image Retrieval is a challenging problem both in terms of effectiveness and efficiency. In this paper, we present a flexible cluster-and-search approach that is able to reuse any previously proposed image descriptor as long as a suitable similarity function is provided. In the clustering step, the image data set is clustered using a hybrid divisiveagglomerative hierarchical clustering technique. The obtained clusters are organized in a tree that can be traversed efficiently using the similarity function associated with the chosen image descriptors. Our experiments have shown that we can improve search-time performance by a factor of 10 or more, at the cost of small loss in effectiveness (typically less than 15%) when compared to the state-of-the-art solutions.

• Type: Conference
• Year: 2008
• Where: ACIVS
• Authors: Anderson Rocha, Jurandy Almeida, Mario A. Nascimento, Ricardo da Silva Torres, Siome Goldenstein
• Keywords: ACIVS 2008, Computer Vision, Content-based Image Retrieval, Image Descriptors, Similarity Function